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| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ngupta23/6c044153e5c1cb93663d84f8bf082dd0/pycaret_ts_white_noise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
| "cell_type": "code", | |
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| "base_uri": "https://localhost:8080/" | |
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| "id": "3UPno1ogeICu", | |
| "outputId": "092ec5d8-1e47-4235-bd50-b512c3486c77" | |
| }, | |
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| "#### Install library (one time) ----\n", | |
| "!pip install pycaret-ts-alpha -U" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting pycaret-ts-alpha\n", | |
| " Downloading pycaret_ts_alpha-3.0.0.dev1636427886-py3-none-any.whl (479 kB)\n", | |
| "\u001b[K |████████████████████████████████| 479 kB 10.0 MB/s \n", | |
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| " Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "Successfully built htmlmin imagehash alembic databricks-cli pyLDAvis pyod umap-learn pynndescent\n", | |
| "Installing collected packages: threadpoolctl, tangled-up-in-unicode, smmap, scipy, multimethod, llvmlite, joblib, websocket-client, visions, tenacity, statsmodels, scikit-learn, requests, python-editor, numba, Mako, imagehash, gitdb, querystring-parser, pyyaml, pynndescent, pydantic, prometheus-flask-exporter, pmdarima, plotly, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, yellowbrick, umap-learn, tbats, sktime, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret-ts-alpha\n", | |
| " Attempting uninstall: scipy\n", | |
| " Found existing installation: scipy 1.4.1\n", | |
| " Uninstalling scipy-1.4.1:\n", | |
| " Successfully uninstalled scipy-1.4.1\n", | |
| " Attempting uninstall: llvmlite\n", | |
| " Found existing installation: llvmlite 0.34.0\n", | |
| " Uninstalling llvmlite-0.34.0:\n", | |
| " Successfully uninstalled llvmlite-0.34.0\n", | |
| " Attempting uninstall: joblib\n", | |
| " Found existing installation: joblib 1.1.0\n", | |
| " Uninstalling joblib-1.1.0:\n", | |
| " Successfully uninstalled joblib-1.1.0\n", | |
| " Attempting uninstall: statsmodels\n", | |
| " Found existing installation: statsmodels 0.10.2\n", | |
| " Uninstalling statsmodels-0.10.2:\n", | |
| " Successfully uninstalled statsmodels-0.10.2\n", | |
| " Attempting uninstall: scikit-learn\n", | |
| " Found existing installation: scikit-learn 0.22.2.post1\n", | |
| " Uninstalling scikit-learn-0.22.2.post1:\n", | |
| " Successfully uninstalled scikit-learn-0.22.2.post1\n", | |
| " Attempting uninstall: requests\n", | |
| " Found existing installation: requests 2.23.0\n", | |
| " Uninstalling requests-2.23.0:\n", | |
| " Successfully uninstalled requests-2.23.0\n", | |
| " Attempting uninstall: numba\n", | |
| " Found existing installation: numba 0.51.2\n", | |
| " Uninstalling numba-0.51.2:\n", | |
| " Successfully uninstalled numba-0.51.2\n", | |
| " Attempting uninstall: pyyaml\n", | |
| " Found existing installation: PyYAML 3.13\n", | |
| " Uninstalling PyYAML-3.13:\n", | |
| " Successfully uninstalled PyYAML-3.13\n", | |
| " Attempting uninstall: plotly\n", | |
| " Found existing installation: plotly 4.4.1\n", | |
| " Uninstalling plotly-4.4.1:\n", | |
| " Successfully uninstalled plotly-4.4.1\n", | |
| " Attempting uninstall: yellowbrick\n", | |
| " Found existing installation: yellowbrick 0.9.1\n", | |
| " Uninstalling yellowbrick-0.9.1:\n", | |
| " Successfully uninstalled yellowbrick-0.9.1\n", | |
| " Attempting uninstall: pandas-profiling\n", | |
| " Found existing installation: pandas-profiling 1.4.1\n", | |
| " Uninstalling pandas-profiling-1.4.1:\n", | |
| " Successfully uninstalled pandas-profiling-1.4.1\n", | |
| " Attempting uninstall: mlxtend\n", | |
| " Found existing installation: mlxtend 0.14.0\n", | |
| " Uninstalling mlxtend-0.14.0:\n", | |
| " Successfully uninstalled mlxtend-0.14.0\n", | |
| " Attempting uninstall: lightgbm\n", | |
| " Found existing installation: lightgbm 2.2.3\n", | |
| " Uninstalling lightgbm-2.2.3:\n", | |
| " Successfully uninstalled lightgbm-2.2.3\n", | |
| " Attempting uninstall: imbalanced-learn\n", | |
| " Found existing installation: imbalanced-learn 0.4.3\n", | |
| " Uninstalling imbalanced-learn-0.4.3:\n", | |
| " Successfully uninstalled imbalanced-learn-0.4.3\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n", | |
| "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", | |
| "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", | |
| "Successfully installed Boruta-0.3 Mako-1.1.5 alembic-1.4.1 databricks-cli-0.16.2 docker-5.0.3 funcy-1.16 gitdb-4.0.9 gitpython-3.1.24 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 joblib-1.0.1 kmodes-0.11.1 lightgbm-3.3.1 llvmlite-0.37.0 mlflow-1.21.0 mlxtend-0.19.0 multimethod-1.6 numba-0.54.1 pandas-profiling-3.1.0 phik-0.12.0 plotly-5.3.1 pmdarima-1.8.4 prometheus-flask-exporter-0.18.5 pyLDAvis-3.2.2 pycaret-ts-alpha-3.0.0.dev1636427886 pydantic-1.8.2 pynndescent-0.5.5 pyod-0.9.5 python-editor-1.0.4 pyyaml-5.4.1 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.24.2 scikit-plot-0.3.7 scipy-1.5.4 sktime-0.8.1 smmap-5.0.0 statsmodels-0.12.1 tangled-up-in-unicode-0.1.0 tbats-1.1.0 tenacity-8.0.1 threadpoolctl-3.0.0 umap-learn-0.5.2 visions-0.7.4 websocket-client-1.2.1 yellowbrick-1.3.post1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "XRZkrqAeeLV1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "5cca7673-15ab-452c-c4d2-c917073966d2" | |
| }, | |
| "source": [ | |
| "#### Import libraries ----\n", | |
| "from pycaret.datasets import get_data\n", | |
| "from pycaret.internal.pycaret_experiment import TimeSeriesExperiment" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", | |
| " defaults = yaml.load(f)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "vk9WxocReL_1", | |
| "outputId": "ec1eb8ac-329f-4afe-8ca2-238bd11a323e" | |
| }, | |
| "source": [ | |
| "#### Get the data ---\n", | |
| "y = get_data(\"1\", folder=\"time_series/white_noise\")" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
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| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 695, | |
| "referenced_widgets": [ | |
| "8d33c4b0a9cc44a98dc9f7770071d9ac", | |
| "6104c0fcf66145969af361f0395e0790", | |
| "d84593573afa4fa695659e0acfea1ffb" | |
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| }, | |
| "id": "jrNi5LiReTUX", | |
| "outputId": "c5d38415-8d78-4b7e-cf29-84695e94e708" | |
| }, | |
| "source": [ | |
| "#### Setup the experiment ----\n", | |
| "exp = TimeSeriesExperiment()\n", | |
| "exp.setup(data=y, fh=30, seasonal_period=1, session_id=42)" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Description</th>\n", | |
| " <th>Value</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>session_id</td>\n", | |
| " <td>42</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Original Data</td>\n", | |
| " <td>(340, 1)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Missing Values</td>\n", | |
| " <td>False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Transformed Train Set</td>\n", | |
| " <td>(310,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Transformed Test Set</td>\n", | |
| " <td>(30,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Fold Generator</td>\n", | |
| " <td>ExpandingWindowSplitter</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Fold Number</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>Enforce Prediction Interval</td>\n", | |
| " <td>False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>Seasonal Period Tested</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>Seasonality Detected</td>\n", | |
| " <td>False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>Target Strictly Positive</td>\n", | |
| " <td>True</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>Target White Noise</td>\n", | |
| " <td>Yes</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>Recommended d</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>Recommended Seasonal D</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>CPU Jobs</td>\n", | |
| " <td>-1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>Use GPU</td>\n", | |
| " <td>False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16</th>\n", | |
| " <td>Log Experiment</td>\n", | |
| " <td>False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17</th>\n", | |
| " <td>Experiment Name</td>\n", | |
| " <td>ts-default-name</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>USI</td>\n", | |
| " <td>cc17</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>19</th>\n", | |
| " <td>Imputation Type</td>\n", | |
| " <td>simple</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Description Value\n", | |
| "0 session_id 42\n", | |
| "1 Original Data (340, 1)\n", | |
| "2 Missing Values False\n", | |
| "3 Transformed Train Set (310,)\n", | |
| "4 Transformed Test Set (30,)\n", | |
| "5 Fold Generator ExpandingWindowSplitter\n", | |
| "6 Fold Number 3\n", | |
| "7 Enforce Prediction Interval False\n", | |
| "8 Seasonal Period Tested 1\n", | |
| "9 Seasonality Detected False\n", | |
| "10 Target Strictly Positive True\n", | |
| "11 Target White Noise Yes\n", | |
| "12 Recommended d 0\n", | |
| "13 Recommended Seasonal D 0\n", | |
| "14 CPU Jobs -1\n", | |
| "15 Use GPU False\n", | |
| "16 Log Experiment False\n", | |
| "17 Experiment Name ts-default-name\n", | |
| "18 USI cc17\n", | |
| "19 Imputation Type simple" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f3281e5ba50>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9HFWV90FuFDp" | |
| }, | |
| "source": [ | |
| "When setup is run, some exploratory analysis is automatically done in the background (e.g. Ljung-Box test is run for K=24 and 48 by default) and the results are presented for the modeler. In this case, pycaret diagnosed this as white noise data.\n", | |
| "\n", | |
| "In addition, modelers can perform their own exploratory analysis using the interactive EDA capability available in pycaret" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "tytubi4duoFC" | |
| }, | |
| "source": [ | |
| "## Exploratory Analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 542 | |
| }, | |
| "id": "6QZhVf1yeVG3", | |
| "outputId": "2dec96ed-8512-466e-e12d-ef1403c4ec66" | |
| }, | |
| "source": [ | |
| "#### Plot the dataset & its ACF ----\n", | |
| "exp.plot_model(plot=\"diagnostics\")" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<html>\n", | |
| "<head><meta charset=\"utf-8\" /></head>\n", | |
| "<body>\n", | |
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| " Plotly.purge(gd);\n", | |
| " observer.disconnect();\n", | |
| " }}\n", | |
| "}});\n", | |
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| "// Listen for the removal of the full notebook cells\n", | |
| "var notebookContainer = gd.closest('#notebook-container');\n", | |
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| " x.observe(notebookContainer, {childList: true});\n", | |
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| " x.observe(outputEl, {childList: true});\n", | |
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| " }) }; </script> </div>\n", | |
| "</body>\n", | |
| "</html>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
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| }, | |
| "source": [ | |
| "Again, from the diagnostic plots, we can see that the histogram, QQ plot and ACF all seem to indicate that the data is consistent with white noise with a mean of ~ 176.\n", | |
| "\n", | |
| "We noted above that Ljung-Box test was performed during the setup phase. Modelers can get the details of this test using the following" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 238 | |
| }, | |
| "id": "ShsoIspweVtm", | |
| "outputId": "2ee77396-52fa-43d7-cf69-85640cf4b916" | |
| }, | |
| "source": [ | |
| "exp.check_stats(test=\"white_noise\")" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Test</th>\n", | |
| " <th>Test Name</th>\n", | |
| " <th>Property</th>\n", | |
| " <th>Setting</th>\n", | |
| " <th>Value</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>Test Statictic</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>28.0627</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>Test Statictic</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>48.3828</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>p-value</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>0.257406</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>p-value</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>0.457383</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>True</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>True</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Test Test Name Property Setting Value\n", | |
| "0 White Noise Ljung-Box Test Statictic {'alpha': 0.05, 'K': 24} 28.0627\n", | |
| "1 White Noise Ljung-Box Test Statictic {'alpha': 0.05, 'K': 48} 48.3828\n", | |
| "2 White Noise Ljung-Box p-value {'alpha': 0.05, 'K': 24} 0.257406\n", | |
| "3 White Noise Ljung-Box p-value {'alpha': 0.05, 'K': 48} 0.457383\n", | |
| "4 White Noise Ljung-Box White Noise {'alpha': 0.05, 'K': 24} True\n", | |
| "5 White Noise Ljung-Box White Noise {'alpha': 0.05, 'K': 48} True" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "EXbV8z3QuYV-" | |
| }, | |
| "source": [ | |
| "As we can see, at a significance level α = 0.05, we fail to reject the null hypothesis for both K=24 and K=48 since the p-value is greater than 0.05.\n", | |
| "\n", | |
| "All this exploratory analysis indicates that the data is consistent with White Noise!\n", | |
| "\n", | |
| "Now let's see how we can model this dataset..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xlHHDsWBucWx" | |
| }, | |
| "source": [ | |
| "## Modeling White Noise\n", | |
| "\n", | |
| "It is best to model this kind of time series data set with a naive (also known as Grand Means) model where the prediction is simply the mean value of the data used to train the model. This is the \"best\" estimate of any future prediction when the data is white noise. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206, | |
| "referenced_widgets": [ | |
| "f6ca10eee5f0471cbcd27ed62e033f4d", | |
| "32f5c705f6e24913a792dcfef27a5bd4", | |
| "47fd261f70b84126b0ac3f5be22012da" | |
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| "id": "2Lpp5omReX5W", | |
| "outputId": "98d01b7c-876b-4f4f-d571-23a1762c7c47" | |
| }, | |
| "source": [ | |
| "model = exp.create_model(\"grand_means\")" | |
| ], | |
| "execution_count": 245, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>cutoff</th>\n", | |
| " <th>MAE</th>\n", | |
| " <th>RMSE</th>\n", | |
| " <th>MAPE</th>\n", | |
| " <th>SMAPE</th>\n", | |
| " <th>R2</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1466.0</td>\n", | |
| " <td>1.0584</td>\n", | |
| " <td>1.2479</td>\n", | |
| " <td>0.0012</td>\n", | |
| " <td>0.0012</td>\n", | |
| " <td>-0.0025</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1496.0</td>\n", | |
| " <td>0.7452</td>\n", | |
| " <td>0.9944</td>\n", | |
| " <td>0.0008</td>\n", | |
| " <td>0.0008</td>\n", | |
| " <td>-0.0121</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1526.0</td>\n", | |
| " <td>0.9018</td>\n", | |
| " <td>1.0852</td>\n", | |
| " <td>0.0010</td>\n", | |
| " <td>0.0010</td>\n", | |
| " <td>-0.0006</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mean</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.9018</td>\n", | |
| " <td>1.1091</td>\n", | |
| " <td>0.0010</td>\n", | |
| " <td>0.0010</td>\n", | |
| " <td>-0.0051</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>SD</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.1279</td>\n", | |
| " <td>0.1049</td>\n", | |
| " <td>0.0001</td>\n", | |
| " <td>0.0001</td>\n", | |
| " <td>0.0050</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
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| "</div>" | |
| ], | |
| "text/plain": [ | |
| " cutoff MAE RMSE MAPE SMAPE R2\n", | |
| "0 1466.0 1.0584 1.2479 0.0012 0.0012 -0.0025\n", | |
| "1 1496.0 0.7452 0.9944 0.0008 0.0008 -0.0121\n", | |
| "2 1526.0 0.9018 1.0852 0.0010 0.0010 -0.0006\n", | |
| "Mean NaN 0.9018 1.1091 0.0010 0.0010 -0.0051\n", | |
| "SD NaN 0.1279 0.1049 0.0001 0.0001 0.0050" | |
| ] | |
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| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QcW424oHu1LB" | |
| }, | |
| "source": [ | |
| "#### Check Model Fit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
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| "id": "s9nsQmZceyh3", | |
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| "source": [ | |
| "#### Passing an estimator to `check_stats` applies the test to the model residuals\n", | |
| "exp.check_stats(model, \"white_noise\")" | |
| ], | |
| "execution_count": 246, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
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| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Test</th>\n", | |
| " <th>Test Name</th>\n", | |
| " <th>Property</th>\n", | |
| " <th>Setting</th>\n", | |
| " <th>Value</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>Test Statictic</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>32.7716</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>Test Statictic</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>51.0016</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>p-value</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>0.108984</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>p-value</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>0.356455</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 24}</td>\n", | |
| " <td>True</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>Ljung-Box</td>\n", | |
| " <td>White Noise</td>\n", | |
| " <td>{'alpha': 0.05, 'K': 48}</td>\n", | |
| " <td>True</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Test Test Name Property Setting Value\n", | |
| "0 White Noise Ljung-Box Test Statictic {'alpha': 0.05, 'K': 24} 32.7716\n", | |
| "1 White Noise Ljung-Box Test Statictic {'alpha': 0.05, 'K': 48} 51.0016\n", | |
| "2 White Noise Ljung-Box p-value {'alpha': 0.05, 'K': 24} 0.108984\n", | |
| "3 White Noise Ljung-Box p-value {'alpha': 0.05, 'K': 48} 0.356455\n", | |
| "4 White Noise Ljung-Box White Noise {'alpha': 0.05, 'K': 24} True\n", | |
| "5 White Noise Ljung-Box White Noise {'alpha': 0.05, 'K': 48} True" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 246 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "y0MGSVn_vaCJ" | |
| }, | |
| "source": [ | |
| "The residuals from the model fit are consistent with white noise as well. Hence the fit seems to be good!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 542 | |
| }, | |
| "id": "FMEKodB2e5kh", | |
| "outputId": "3a51287e-a053-4ec3-f9a0-c802622665c4" | |
| }, | |
| "source": [ | |
| "#### Passing an estimator to `plot_model` applies the plot to the model residuals\n", | |
| "exp.plot_model(model, \"diagnostics\")" | |
| ], | |
| "execution_count": 247, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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